Cancer remains one of the leading global health burdens, with increasing complexity in genomic, imaging, and clinical datasets presenting significant challenges for effective management. Artificial intelligence (AI) has emerged as a powerful tool to address these challenges by enabling pattern recognition, knowledge integration, and data-driven decision-making. This review highlights recent advances in the application of AI across cancer research, diagnosis, and therapy. In research, AI accelerates drug discovery and repurposing, enhances genomic data interpretation, and facilitates biomarker identification through multi-omics integration. In diagnosis, AI has demonstrated high technical performance in radiology for lesion detection and image segmentation, in pathology for tumour grading and molecular prediction, and in liquid biopsy for non-invasive biomarker analysis. In therapy, AI supports precision medicine by predicting treatment responses, monitoring disease progression, and optimizing clinical trial design. Despite these advances, barriers such as data heterogeneity, algorithmic bias, interpretability, and regulatory challenges remain. Future directions, including explainable AI, federated learning, multimodal modelling, and digital twins, hold promise for translating AI-driven innovations into routine oncology practice. Significance Statement This review provides a timely synthesis of recent (2020–2025) advances in artificial intelligence across cancer research, diagnosis, and therapy, highlighting applications in drug discovery, genomics, multi-omics biomarker identification, and clinical decision-making. By integrating technological progress with translational and clinical relevance, this work serves as a valuable resource for bridging AI innovation with precision oncology practice. As a narrative review, the literature was identified through targeted PubMed, Scopus, and Google Scholar searches, combining terms for artificial intelligence, machine learning, and deep learning with cancer-related keywords, with priority given to peer-reviewed studies published between 2020 and 2025, seminal earlier works, and official regulatory or guideline documents. Within each domain, representative studies were selected to illustrate methodological diversity, clinical context, and current translational readiness rather than to provide exhaustive coverage of an extremely rapidly evolving field.
This study examines the impact of Artificial Intelligence Capability (AIC) on Corporate Financial Performance (CFP) through the mediating role of Financial Decision-Making Quality (FDMQ). A simulation-based quantitative explanatory design was applied to 200 computer-generated Likert-scale observations calibrated to represent finance and accounting decision contexts in AI-enabled organizations. The analysis uses instrument validity and reliability testing, multiple linear regression, and mediation analysis following PROCESS Model 4 logic with 5,000 bootstrap resamples in IBM SPSS Statistics. The results show that AIC positively affects FDMQ (β = 0.617, p < 0.001) and retains a significant direct effect on CFP after the mediator is included (β = 0.316, p < 0.001). FDMQ also has a positive effect on CFP (β = 0.473, p < 0.001), while the outcome model explains 50.8% of the variance in CFP. The indirect effect is significant (B = 0.319; 95% bootstrap CI [0.231, 0.412]), indicating partial mediation. The findings support the proposed mechanism that AI capability creates financial value when technological resources are translated into timely, evidence-based, and economically sound financial decisions.
Fera Lufhidarani Pranita, Mohammad Sigit adi Nugraha, Maria Evy Purwitasari· Jurnal Publikasi Ilmu Manaje...· 0 citations
Purpose: This study aims to examine how dynamic capabilities—detecting, utilizing, and transforming—mediate the adoption of Generative Artificial Intelligence (Generative AI) to optimize content marketing strategies among culinary Micro, Small, and Medium Enterprises (MSMEs) in Jatinangor. Research Method: A qualitative descriptive approach with a multiple case study design was employed to explore how culinary MSMEs integrate Generative AI into content marketing while addressing digital and resource constraints. Results and Discussion: The findings show that detecting capability develops organically through a bottom-up process, with creative staff acting as information gatekeepers. This capability is reflected in tactical budgeting for premium AI accounts and independent experimentation. Transforming capability emerges by restructuring conventional workflows into a human–AI hybrid model, in which AI generates ideas and drafts, while creative staff perform cultural and local curation. This integration reduces content production time by 50%–60% without compromising brand authenticity or local identity. Implications: Generative AI serves as a capability enhancer, increasing creative productivity and supporting digital creativity among resource-constrained MSMEs. Originality: This study contributes by explaining Generative AI adoption through a dynamic capabilities perspective and demonstrating how human–AI collaboration enables productive yet culturally authentic content marketing at the micro-business level.
Anthonius S. Hutabarat, Dewi Tamara, Irawan R D Budianto et al.· Advances in Human Resource M...· 0 citations
This article explains how ontology-based semantic layers work and advocates for their use as versatile enterprise tools. They add shared meaning, context, and governance to data, improving integration, analytics, oversight, and AI reliability across organizational systems.
Sally Hubbard, Elena Loukoianova, Hsiao-Ying Lin· Computer· 0 citations
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Transformasi digital layanan publik melalui aplikasi M-Paspor menghadapi tantangan signifikan terkait stabilitas sistem dan kepuasan pengguna. Melalui pendekatan Explainable AI, metode SHAP diimplementasikan untuk membongkar mekanisme internal algoritma XGBoost, untuk mengidentifikasi kontributor utama sentimen negatif secara visual dan terukur. Berdasarkan hasil algoritma XGBoost dengan akurasi 92%, melalui pendekatan XAI dengan metode SHAP. Penelitian ini mengintegrasikan model XGBoost dengan metode SHAP yang menghasilkan identifikasi kuantitatif fitur dominan. Kata positif yaitu “lancar” (1,80), “mantap” (1,22), “membantu” (1,02) serta kata negatif “lambat” (-0,77), “mempersulit” (-0,73), “buruk” (-0,43), yang mengindikasikan adanya kendala pada sistem verifikasi dan alur antarmuka. Temuan ini selanjutnya diformulasikan ke dalam kerangka kerja strategis yang merekomendasikan prioritas perbaikan pada aspek kecepatan respons sistem, penyederhanaan prosedur, dan stabilitas teknis, yang terbukti sebagai pemicu utama sentimen negatif pada nilai SHAP: lambat = -0,77, mempersulit = -0,73, buruk = -0,43, sehingga instansi dapat meningkatkan kualitas pelayanan publik digital secara lebih terarah dan berorientasi pada pengguna.
Hagi Semara, Putera, Made Lanang et al.· Jurnal Nasional Teknologi da...· 0 citations
The findings indicate the feasibility of the BERT model as an initial, human-in-the-loop screening stage for ethical data governance in the public sector.
Gomes Braz Ingrit dos Anjos, Luiz Augusto de Oliveira Almeida, Ueuder Castro Do Nascimentoa et al.· EGOV-CeDEM-ePart 2026· 0 citations
KORA proposes the concept of Data Space Lite: lightweight semantic infrastructures based on W3C standards and Data Spaces principles for rapidly deploying reusable interoperability layers in GovTech environments.
Mélida López, Francisco Sáez, Bárbara Ribeiro et al.· EGOV-CeDEM-ePart 2026· 0 citations
InureFusionNet is proposed, a hybrid explainable structured ensemble framework integrating heterogeneous models of deep feature learning, uncertainty-aware prediction, interpretable boosting mechanisms, and high-performance gradient- boosting classifiers through a fusion strategy for robust health insurance claim approval classification.
Indirakumar Rajendiran, Sam Cherub Hameem Pillay· International Journal of Com...· 0 citations
The findings reveal that service quality positively affected AI source credibility, brand trust, and purchase intention, and suggest that trust in AI systems can be transferred to brand trust.
Nameun Kim, Hyojin Jo· Journal of the Korean Societ...· 0 citations
The next generation of mobile networks is envisioned as fully AI-native, with AI-RAN architectures embedding small language models (SLMs) to perform reasoning over real-time telemetry. The state-of-the-art training paradigms for telecom LLMs, exemplified by RANSTRUCT-style supervised fine-tuning (SFT) on curated instruction data, are limited to post hoc rationalization. Here, the explanations, when produced at all, are generated after or independently of the decision, leaving the decision process unauditable. Pre-hoc reasoning, where a causal reasoning trace is produced before the output label, is preferable, and the broader LLM reasoning literature has made real progress toward it via RL methods such as Group Relative Policy Optimization (GRPO). Here we observe that transplanting this recipe into the telecom setting runs into a cold-start barrier: SLMs either learn to output the desired format or learn to predict the label, but rarely both. We identify this barrier and propose CRAFT, which stands for Cold-start Reasoning Alignment via Fine-Tuning, a data-centric method to autonomously generate a verified dataset of (input, trace, label) triplets. CRAFT fine-tunes SLMs on this verified data using low-rank adaptation (LoRA), requiring substantially less compute and wall-clock time than GRPO-based methods. On the TRACTOR and IC xApp telecom datasets, CRAFT achieves up to 86.5% and 94.6% for accuracy and F1 with no parse failures, while direct GRPO and SFT+GRPO fail to exceed 28% and 53.5% F1 with multiple parse failures. We further show that CRAFT-initialized policies serve as a robust foundation for subsequent GRPO fine-tuning, as under diverse reward functions the performance remains consistent with no parse failures. Finally, we demonstrate that CRAFT consumes 59% less energy than GRPO-based baselines, making it a sustainable path to deployable, auditable AI in 6G RAN.
Machine learning systems deployed for credit, hiring, and resource distribution are increasingly subject to regulatory oversight from policies such as the EU AI Act and GDPR. Current fairness governance practices rely on observational fairness metrics, post-hoc explainability, and immutable audit logs, but provide limited support for causal attribution and efficient evidentiary verification. We introduce Causal Evidentiary Governance (CEG), a framework in which regulated institutions commit to a versioned directed acyclic graph (DAG) that partitions causal pathways into allowable and disallowed groups. The Causal Harm Rate measures prediction variation attributable to disallowed causal pathways. Each decision is accompanied by a signed Decision-Evidence Packet (DEP), cryptographically binding the prediction to a digest of the published DAG and path-specific attributions. DEP digests can be appended to a Merkle tree to enable logarithmic-cost inclusion proofs. We validate CEG through a two-layer empirical methodology using demographic summaries from four years of PMA credit supervisory data to construct 10,000 synthetic credit applicants across four strategic DAG counterfactuals. Causal Harm Rate isolates injected causal effects more clearly than demographic parity or equalized odds. Cross-model validation and ablation studies assess robustness. Evaluation on the German Credit dataset shows that harm associated with specific causal pathways can be substantially understated by associational fairness metrics. Finally, a proof-of-concept implementation demonstrates operationally plausible throughput and highlights relevant performance tradeoffs.
A new method, called CW-Net, translates the reasoning process of an autonomous vehicle’s AI system into understandable concepts that explain its behavior.
